Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 561659
Title Polsarnet: A deep fully convolutional network for polarimetric sar image classification
Author(s) Mullissa, Adugna G.; Persello, Claudio; Stein, Alfred
Source IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (2019)12. - ISSN 1939-1404 - p. 5300 - 5309.
DOI https://doi.org/10.1109/JSTARS.2019.2956650
Department(s) Laboratory of Geo-information Science and Remote Sensing
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) Convolutional neural network (CNN) - deep learning - image classification - machine learning - polarimetric SAR (PolSAR)
Abstract

Deep learning has successfully improved the classification accuracy of optical remote sensing images. Recent works attempted to transfer the success of these techniques to the microwave domain to classify Polarimetric SAR data. So far, most deep learning networks separate amplitude and phase as separate input images. In this article, we present a deep fully convolutional network that uses real-valued weight kernels to perform pixel-wise classification of complex-valued images. We evaluated the performance of this network by comparing it with support vector machine, Random Forest, complex-valued convolutional neural network (CV-CNN), and a network that uses amplitude and phase information separately as real channels. The evaluation was done on a quad-polarized AIRSAR image and a dual-polarimetric multitemporal Sentinel-1 data acquired over Flevoland, the Netherlands. The proposed method achieved higher accuracy compared to all other networks with the same architecture.

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